I've certainly seen several politicians argue that "radiation is actually good for you," but I've yet to hear any actual radiation health physicists argue that point...
Well, one doesn't usually see any actual radiation health physicists argue anything. I sure seen various engineering type people argue its good, and there are entire countries (Japan) where the linear-no-threshold model is evidently not adhered to.
Plus there is something weird going on with wikipedia articles on the subject all trying to present the pre-LNT views as something new that's challenging the LNT, complete with editing out of highly relevant historical references. Then there is "radiation hormesis", a hypothesis, that the radiation is ...
Nutrition is a case where we have to try to make the best possible use of the data we have no matter how terrible, because we have to eat something now to sustain us while we plan and conduct more experiments.
I want to apply Bayes theorem to make rational health decisions from relatively weak data. I am generally wondering how one can synthesize historical human experiences with incomplete scientific data, in order to make risk-adverse and healthy decisions about human nutrition given limited research.
Example question/hypothesis: Does gluten cause health problems (ie exhibit chronic toxicity) in non-coeliac humans? Is there enough evidence to suggest that avoiding gluten might be a prudent risk-adverse decision for non-coeliacs?
We have some (mostly in vitro) scientific data suggesting that gluten may cause health problems in non-coeliac humans (such as these articles http://evolvify.com/the-case-against-gluten-medical-journal-references/). Let's say for the sake of arguing, that I can somehow convert these studies into a non-unity likelihood ratio for gluten toxicity in humans (although suggestions are welcome here too).
However, we also have prior information that a population of humans has been consuming gluten containing foods for at least 10,000 years, without any blatantly obvious toxic effects. Is there some way to convert this observation (and observations like this) into a prior probability distribution?